Gain insights into ML system design, state-of-the-art techniques, and best practices for scalable production. Learn from top researchers and stand out in your next ML interview.

Machine Learning System Design is an important component of any ML interview. The ability to address problems, identify requirements, and discuss tradeoffs helps you stand out among hundreds of other candidates. Readers of this course able to get offers from Snapchat, Facebook, Coupang, Stitchfix and LinkedIn. 

This course will help you understand the state of the practice on model techniques along with best practices in applying ML models in production at scale. Once you finished the course you can learn more use-cases at: http://mlengineer.io/

Once you're done with the course, you will be able to apply and leverage knowledge from top researchers at tech companies. You will have up to date knowledge in model techniques from hundreds of the latest research and industry papers. There is even a chance that the interviewers will be surprised at the depth of your knowledge.

I really found the quizzes very helpful for testing my ML understanding. Also, the resources shared helped me a lot for revising concepts for my interview preparation. This course will definitely help engineers crack Machine Learning Engineering and Data Science interviews

Senior Data Scientist at Amazon

I have been using your github repo to prep for my interviews and got an offer with NVIDIA with their data science team. Thanks again for your help!

Data Scientist at NVIDIA

I really like what you've built, it'll help a lot of engineers.

MLE at Facebook

It's well organized and the illustrations are well done. Being able to visualize and walk through the steps in order is really helpful in system design. The hints for quizzes is a nice addition, a hint is given if you get stuck in a real interview, mimicking how a real interview 

DS at Fortune 500

Andrew

I just heard back from the recruiter that I passed the Google L5 HC. Thank you very much for sharing the resources on GitHub and for the course on educative.io!

Google Machine Learning Engineer, L5

I got the offer from Intuit. Thanks so much, it would not have been possible without your help.

Senior Machine Learning Engineer, Intuit

I got Google, Facebook, Apple, Tesla, Cruise offer for Senior ML engineer. I thought the course is super helpful. 

Senior Machine Learning Engineer at Cruise

Machine Learning System Design

# Metrics evaluation

In practice, it’s common that the model performs well during offline evaluation but does not perform well when in production. Therefore, it is important to measure model performance in both on and offline environments.

## Offline metrics
- During offline training and evaluating, we use metrics like logloss, #key# MAE: Mean Absolute Error #key#, and #key# R2: Coefficient of determination #key# to measure the goodness of fit. Once the model shows improvement, the next step would be to move to the staging/sandbox environment to test for a small percentage of real traffic. 

## Online metrics

- During the staging phase, we measure certain metrics, such as Lift in revenue or click through rate, to evaluate how well the model recommends relevant content to users. Consequently, we evaluate the impact on business metrics. If the observed revenue-related metrics show consistent improvement, then it is safe to gradually expose the model to a larger percentage of real traffic. Finally, when we have enough evidence that new models have improved revenue metrics, we can replace the current production models with new models. For further reading, explore how [Sage Maker enables A/B testing](https://aws.amazon.com/blogs/machine-learning/a-b-testing-ml-models-in-production-using-amazon-sagemaker/)
or [LinkedIn A/B testing](https://engineering.linkedin.com/blog/2015/11/top-five-lessons-from-running-a-b-tests-on-the-world-s-largest-p). 

- This diagram shows one way to allocate traffic to different models in production. In reality, there will be few a dozen models, each getting real traffic to serve online requests. This is one way to verify whether or not a model actually generates lift in the production environment. 



Learn different metrics for model evaluations. 

Metrics Evaluation

Learn different metrics for model evaluations.

Machine Learning Primer

Video Recommendation

Feed Ranking

Ad Click Prediction

Rental Search Ranking

Estimate Food Delivery Time

Conclusion

Machine Learning Knowledge

Machine Learning Model Diagnosis

Metrics Evaluation

Metrics evaluation

Offline metrics